import json
import sys
import pandas as pd
from pathlib import Path
from itertools import repeat
from collections import OrderedDict

if sys.version_info[0] > 2:
    is_py3 = True
else:
    reload(sys)
    sys.setdefaultencoding("utf-8")
    is_py3 = False


def native_content(content):
    if not is_py3:
        return content.decode('utf-8')
    else:
        return content


def open_file(filename, mode='r'):
    """
    常用文件操作，可在python2和python3间切换.
    mode: 'r' or 'w' for read or write
    """
    if is_py3:
        return open(filename, mode, encoding='utf-8', errors='ignore')
    else:
        return open(filename, mode)


def read_txt_by_tab(filename):
    """读取文件数据"""
    labels, contents = [], []
    with open_file(filename) as f:
        for line in f:
            try:
                label, content = line.strip().split('\t')
                if len(content) > 0:
                    labels.append(native_content(label))
                    contents.append(native_content(content).strip().split(' '))
            except:
                pass
    return labels, contents


def ensure_dir(dirname):
    dirname = Path(dirname)
    if not dirname.is_dir():
        dirname.mkdir(parents=True, exist_ok=False)


def read_json(fname):
    fname = Path(fname)
    with fname.open('rt') as handle:
        return json.load(handle, object_hook=OrderedDict)


def write_json(content, fname):
    fname = Path(fname)
    with fname.open('wt') as handle:
        json.dump(content, handle, indent=4, sort_keys=False)


def inf_loop(data_loader):
    ''' wrapper function for endless data loader. '''
    for loader in repeat(data_loader):
        yield from loader


class MetricTracker:
    def __init__(self, *keys, writer=None):
        self.writer = writer
        self._data = pd.DataFrame(index=keys, columns=['total', 'counts', 'average'])
        self.reset()

    def reset(self):
        for col in self._data.columns:
            self._data[col].values[:] = 0

    def update(self, key, value, n=1):
        if self.writer is not None:
            self.writer.add_scalar(key, value)
        self._data.total[key] += value * n
        self._data.counts[key] += n
        self._data.average[key] = self._data.total[key] / self._data.counts[key]

    def avg(self, key):
        return self._data.average[key]

    def result(self):
        return dict(self._data.average)
